{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2044 entries, 0 to 2043\n",
      "Data columns (total 3 columns):\n",
      "Unnamed: 0             2044 non-null int64\n",
      "title                  2044 non-null object\n",
      "isupliftinganecdote    2044 non-null float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 48.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "nrc = pd.read_excel(\"NRCEmotionLexicon.xlsx\")\n",
    "content = pd.read_csv(\"labeleddataformodel.csv\")\n",
    "print(content.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrc = nrc[[\"English (en)\", \"Anger\", \"Anticipation\", \"Disgust\", \"Fear\", \"Joy\", \"Sadness\", \"Surprise\", \"Trust\"]]\n",
    "content = content[[\"title\", \"isupliftinganecdote\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "\n",
    "\n",
    "def makeemotioncsvforrange(x,y):\n",
    "    originalframe = content.iloc[x:y]\n",
    "    angercolumn = []\n",
    "    anticipationcolumn = []\n",
    "    disgustcolumn = []\n",
    "    fearcolumn = []\n",
    "    joycolumn = []\n",
    "    sadnesscolumn =[]\n",
    "    surprisecolumn = []\n",
    "    trustcolumn = []\n",
    "    for i in range(x,y):\n",
    "        print(i)\n",
    "        string = str(content[\"title\"][i])\n",
    "        string = string.lower()\n",
    "        titlewords = tokenizer.tokenize(string)\n",
    "        lineanger = 0\n",
    "        lineanticipation = 0\n",
    "        linedisgust = 0\n",
    "        linefear = 0\n",
    "        linejoy = 0\n",
    "        linesadness = 0\n",
    "        linesurprise = 0\n",
    "        linetrust = 0\n",
    "        for word in titlewords:\n",
    "            for q in range(0,14182):\n",
    "                emoword = nrc['English (en)'][q]\n",
    "                emoword = str(emoword)\n",
    "                if word == emoword:\n",
    "                    lineanger += (nrc[\"Anger\"][q])\n",
    "                    lineanticipation += (nrc[\"Anticipation\"][q])\n",
    "                    linedisgust += (nrc[\"Disgust\"][q])\n",
    "                    linefear += (nrc[\"Fear\"][q])\n",
    "                    linejoy += (nrc[\"Joy\"][q])\n",
    "                    linesadness += (nrc[\"Sadness\"][q])\n",
    "                    linesurprise += (nrc[\"Surprise\"][q])\n",
    "                    linetrust += (nrc[\"Trust\"][q])\n",
    "        angercolumn.append(lineanger)\n",
    "        anticipationcolumn.append(lineanticipation)\n",
    "        disgustcolumn.append(linedisgust)\n",
    "        fearcolumn.append(linefear)\n",
    "        joycolumn.append(linejoy)\n",
    "        sadnesscolumn.append(linesadness)\n",
    "        surprisecolumn.append(linesurprise)\n",
    "        trustcolumn.append(linetrust)\n",
    "    originalframe['Anger'] = angercolumn\n",
    "    originalframe['Anticipation'] = anticipationcolumn\n",
    "    originalframe['Disgust'] = disgustcolumn\n",
    "    originalframe['Fear'] = fearcolumn\n",
    "    originalframe['Joy'] = joycolumn\n",
    "    originalframe['Sadness'] = sadnesscolumn\n",
    "    originalframe['Surpise'] = surprisecolumn\n",
    "    originalframe['Trust'] = trustcolumn\n",
    "    print(originalframe.info())\n",
    "    docname = \"/Users/tessmcnulty/labeledcontentemotions.csv\"\n",
    "    originalframe.to_csv(docname)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:50: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:51: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/tessmcnulty/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:57: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2043 entries, 0 to 2042\n",
      "Data columns (total 10 columns):\n",
      "title                  2043 non-null object\n",
      "isupliftinganecdote    2043 non-null float64\n",
      "Anger                  2043 non-null int64\n",
      "Anticipation           2043 non-null int64\n",
      "Disgust                2043 non-null int64\n",
      "Fear                   2043 non-null int64\n",
      "Joy                    2043 non-null int64\n",
      "Sadness                2043 non-null int64\n",
      "Surpise                2043 non-null int64\n",
      "Trust                  2043 non-null int64\n",
      "dtypes: float64(1), int64(8), object(1)\n",
      "memory usage: 159.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "makeemotioncsvforrange(0,2043)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"labeledcontentemotions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2043 entries, 0 to 2042\n",
      "Data columns (total 11 columns):\n",
      "Unnamed: 0             2043 non-null int64\n",
      "title                  2043 non-null object\n",
      "isupliftinganecdote    2043 non-null float64\n",
      "Anger                  2043 non-null int64\n",
      "Anticipation           2043 non-null int64\n",
      "Disgust                2043 non-null int64\n",
      "Fear                   2043 non-null int64\n",
      "Joy                    2043 non-null int64\n",
      "Sadness                2043 non-null int64\n",
      "Surpise                2043 non-null int64\n",
      "Trust                  2043 non-null int64\n",
      "dtypes: float64(1), int64(9), object(1)\n",
      "memory usage: 175.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ua anger anticipation disgust fear joy sadness surprise trust\n",
      "228\n",
      "497\n",
      "176\n",
      "398\n",
      "648\n",
      "327\n",
      "225\n",
      "641\n",
      "nonua anger anticipation disgust fear joy sadness surprise trust\n",
      "273\n",
      "346\n",
      "174\n",
      "396\n",
      "304\n",
      "315\n",
      "274\n",
      "470\n"
     ]
    }
   ],
   "source": [
    "uatotalanger = 0\n",
    "uatotalanticipation = 0\n",
    "uatotaldisgust = 0\n",
    "uatotalfear = 0\n",
    "uatotaljoy = 0\n",
    "uatotalsadness = 0\n",
    "uatotalsurprise = 0\n",
    "uatotaltrust = 0\n",
    "nonuatotalanger = 0\n",
    "nonuatotalanticipation = 0\n",
    "nonuatotaldisgust = 0\n",
    "nonuatotalfear = 0\n",
    "nonuatotaljoy = 0\n",
    "nonuatotalsadness = 0\n",
    "nonuatotalsurprise = 0\n",
    "nonuatotaltrust = 0\n",
    "\n",
    "for i in range (0, 2042):\n",
    "    if df['isupliftinganecdote'][i] == 1:\n",
    "        uatotalanger += df['Anger'][i]\n",
    "        uatotalanticipation += df['Anticipation'][i]\n",
    "        uatotaldisgust += df['Disgust'][i]\n",
    "        uatotalfear += df['Fear'][i]\n",
    "        uatotaljoy += df['Joy'][i]\n",
    "        uatotalsadness += df['Sadness'][i]\n",
    "        uatotalsurprise += df['Surpise'][i]\n",
    "        uatotaltrust += df['Trust'][i]\n",
    "    if df['isupliftinganecdote'][i] == 0:\n",
    "        nonuatotalanger += df['Anger'][i]\n",
    "        nonuatotalanticipation += df['Anticipation'][i]\n",
    "        nonuatotaldisgust += df['Disgust'][i]\n",
    "        nonuatotalfear += df['Fear'][i]\n",
    "        nonuatotaljoy += df['Joy'][i]\n",
    "        nonuatotalsadness += df['Sadness'][i]\n",
    "        nonuatotalsurprise += df['Surpise'][i]\n",
    "        nonuatotaltrust += df['Trust'][i]\n",
    "\n",
    "print(\"ua anger anticipation disgust fear joy sadness surprise trust\")\n",
    "print(uatotalanger)\n",
    "print(uatotalanticipation)\n",
    "print(uatotaldisgust)\n",
    "print(uatotalfear) \n",
    "print(uatotaljoy)\n",
    "print(uatotalsadness)\n",
    "print(uatotalsurprise)\n",
    "print(uatotaltrust)\n",
    "\n",
    "print(\"nonua anger anticipation disgust fear joy sadness surprise trust\")\n",
    "print(nonuatotalanger)\n",
    "print(nonuatotalanticipation)\n",
    "print(nonuatotaldisgust)\n",
    "print(nonuatotalfear)\n",
    "print(nonuatotaljoy)\n",
    "print(nonuatotalsadness)\n",
    "print(nonuatotalsurprise)\n",
    "print(nonuatotaltrust)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c200f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data to plot\n",
    "labels = 'Anger', 'Anticipation', 'Disgust', 'Fear', 'Joy', 'Sadness', 'Surprise', 'Trust'\n",
    "sizes = [228, 497, 176, 398, 648, 327, 225, 641]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'darkorchid', 'crimson', 'darkgreen', 'darkorange']\n",
    "explode = (0, 0, 0, 0, 0, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c10e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data to plot\n",
    "labels = 'Anger', 'Anticipation', 'Disgust', 'Fear', 'Joy', 'Sadness', 'Surprise', 'Trust'\n",
    "sizes = [273, 346, 174, 396, 304, 315, 274, 470]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'darkorchid', 'crimson', 'darkgreen', 'darkorange']\n",
    "explode = (0, 0, 0, 0, 0, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrc = pd.read_excel(\"NRCEmotionLexicon.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14182 entries, 0 to 14181\n",
      "Columns: 115 entries, English (en) to Trust\n",
      "dtypes: int64(10), object(105)\n",
      "memory usage: 12.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(nrc.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anger anticipation disgust fear joy sadness surprise trust\n",
      "1247\n",
      "839\n",
      "1058\n",
      "1476\n",
      "689\n",
      "1191\n",
      "534\n",
      "1231\n"
     ]
    }
   ],
   "source": [
    "totalanger = 0\n",
    "totalanticipation = 0\n",
    "totaldisgust = 0\n",
    "totalfear = 0\n",
    "totaljoy = 0\n",
    "totalsadness = 0\n",
    "totalsurprise = 0\n",
    "totaltrust = 0\n",
    "\n",
    "for i in range (0, 14181):\n",
    "    totalanger += nrc['Anger'][i]\n",
    "    totalanticipation += nrc['Anticipation'][i]\n",
    "    totaldisgust += nrc['Disgust'][i]\n",
    "    totalfear += nrc['Fear'][i]\n",
    "    totaljoy += nrc['Joy'][i]\n",
    "    totalsadness += nrc['Sadness'][i]\n",
    "    totalsurprise += nrc['Surprise'][i]\n",
    "    totaltrust += nrc['Trust'][i]\n",
    "    \n",
    "print(\"anger anticipation disgust fear joy sadness surprise trust\")\n",
    "print(totalanger)\n",
    "print(totalanticipation)\n",
    "print(totaldisgust)\n",
    "print(totalfear) \n",
    "print(totaljoy)\n",
    "print(totalsadness)\n",
    "print(totalsurprise)\n",
    "print(totaltrust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12232f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data to plot\n",
    "labels = 'Anger', 'Anticipation', 'Disgust', 'Fear', 'Joy', 'Sadness', 'Surprise', 'Trust'\n",
    "sizes = [1247, 839, 1058, 1476, 689, 1191, 534, 1231]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'darkorchid', 'crimson', 'darkgreen', 'darkorange']\n",
    "explode = (0, 0, 0, 0, 0, 0, 0, 0)  \n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
